There has been a long history of debate on causality in philosophy, statistics, economics, and related fields. I have been concerned with this classic question--how can we discover causal information from purely observed data (i.e., perform causal inference)? How such causal information can facilitate solving other problems such as modeling, prediction, and control, is also interesting to me.

My research consists of three main lines.

First, I have focused on developing practical computational methods for causal inference, to produce more reliable causal information.

Secondly, to better understand causality and derive more universal methods for causal inference, I also work on finding fundamental and testable principles that help discover causality from data.

Thirdly, latent variable modeling is closely related to causality, and it has been interesting me for over eight years. Developing more general yet identifiable latent variable models would benefit the causality field, as well as the machine learning and signal processing communities.

Since machine learning plays a key role in data analysis as well as causal inference, I am also very interested in this field.

What's New:

We are editing the ACM Transactions on Intelligent Systems and Technologies (ACM TIST) special issue on causal discovery and inference; see the call for papers here. Submission deadline: 14 March 2014.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems